• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Researchers Find More Effective Approach to Revealing Majorana Zero Modes in Superconductors

Researchers Find More Effective Approach to Revealing Majorana Zero Modes in Superconductors

© iStock

An international team of researchers, including physicists from HSE MIEM, has demonstrated that nonmagnetic impurities can help more accurately reveal Majorana zero modes—quantum states considered promising building blocks for quantum computing. The researchers found that these impurities shift the energy levels that typically obscure the Majorana signal, while leaving the mode itself largely unaffected, thereby making its spectral peak more distinct. The study has been published in Research.

Majorana zero modes (MZMs) are rare quantum states that can emerge in certain superconductors. In particular, they form in magnetic vortices—tiny regions within the material where the magnetic field is concentrated. At the same time, MZMs are robust against random disturbances and material impurities, which makes them promising candidates for realising stable qubits—the units of information in a quantum computer. 

The challenge is that MZMs are difficult to detect experimentally: other quantum states with similar energies appear nearby, making the signals easy to confuse. Previous attempts to address this issue focused on selecting superconductors with fewer impurities and fewer extraneous states, in the hope that the Majorana signal would become more pronounced. In practice, however, such systems are difficult to produce and reproduce reliably, and the measurement results often remain ambiguous.

A team of researchers from MIPT, HSE MIEM, MEPhI, and Sorbonne University has shown that a nonmagnetic impurity in a superconductor can serve as a useful element. Using computer modelling, they studied the effect of a defect that creates a local energy barrier and pins the vortex at a specific location. They found that the ordinary states inside the vortex are sensitive to this barrier and shift in energy, while the MZM energy remains unchanged.

As a result, the energy gap between the MZM and the other states increases. Experimentally, this leads to a clearer signal: a pronounced zero-bias peak appears in the spectrum, making it difficult to confuse with other features. Importantly, this approach does not require exotic materials—the effect can also be observed in systems based on commonly used superconductors.

Comparison of a system without an impurity and one with a local impurity at the centre of the vortex. The distribution of the wave function of a low-energy bound state in the non-topological regime (B/F): in the absence of an impurity, the state is more strongly localised at the vortex core, whereas in the presence of an impurity, its profile is significantly modified. The spatial distribution of the modulus of the MZM wave function in the topological regime (C/G): although the impurity alters the spatial profile, the state remains localised in the vortex region. The average local density of states at the vortex centre for both topological and conventional regimes (D/H): without an impurity, low-energy signals strongly overlap, while in the presence of an impurity, the differences between them become more pronounced.
© Vyacheslav D. Neverov, Tairzhan Karabassov, Andrey V. Krasavin, Dimitri Roditchev, Vasily S. Stolyarov, Alexei Vagov. Revealing Majorana Zero Modes in Vortex Cores via Nonmagnetic Impurities. Research. 2026;9:1087.DOI:10.34133/research.1087

Importantly, this effect is observed only in the presence of nonmagnetic impurities. While magnetic impurities suppress superconductivity and interfere with measurements, nonmagnetic impurities, by contrast, can serve as a controllable separator: they push background states away from zero without affecting the MZM.

Alexey Vagov

'Our study offers a more practical approach to generating and detecting MZMs,' explains Alexey Vagov, Director of the HSE MIEM Centre for Quantum Metamaterials and one of the study authors. 'Instead of searching for rare or exotic materials, we rely on controllable defects in more accessible systems. We hope this will accelerate progress toward building a quantum computer based on topological Majorana states.'

The study was conducted with support from the Russian Science Foundation (Project 075-15-2025-608) and the HSE Basic Research Programme

See also:

Neural Network Maps as a Method for Constructing Mathematical Models

Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.

HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality

Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns

The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?

University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.

HSE Study Reveals Imbalance in the Generative AI Market

Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.

‘Entering Robotics Now Means Growing with the Area’

Unmanned vehicles, courier robots, and smart speakers are rapidly becoming a part of our lives. In 2026, the HSE Faculty of Computer Science opens its new Bachelor’s Programme ‘Design of Intelligent Robotic Systems’ (DIRS). It will train specialists at the intersection of IT, artificial intelligence, and robotics. Academic Supervisor of DIRS Vadim Morgachev explains how studies are organised and why graduates of the programme ‘will definitely be accepted into the future.’

HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors

Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.

MIEM Tech Day at Pokrovka: Exploring HSE’s Engineering DNA Together

On May 26, 2026, the central atrium of the building at 11 Pokrovsky Bulvar will host the annual large-scale festival of engineering developments created by project teams from the HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM). The programme includes presentations of the best student technological projects, stands from partner companies and joint workshops, a lecture series featuring practising engineers, a round table on the development of engineering education, and presentations of MIEM master’s degree programmes.

The 'Second Shift' Is Not Why Women Avoid News

Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.

Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty

Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.